
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   inspect.getfile(accelerate.test_utils)
   lowercase__	: Tuple     		=   os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ["""scripts""", """test_script.py"""])
   lowercase__	: Optional[Any]     		=   os.path.sep.join(
       mod_file.split(os.path.sep)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""])
   lowercase__	: int     		=   os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ["""scripts""", """test_ops.py"""])


 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   print(f'Found {torch.cuda.device_count()} devices.')
   lowercase__	: Any     		=   ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
   with patch_environment(omp_num_threads=1):
     execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,	env=os.environ.copy())


 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   print(f'Found {torch.cuda.device_count()} devices.')
   lowercase__	: Tuple     		=   ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
   print(f'Command: {cmd}')
   with patch_environment(omp_num_threads=1):
     execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,	env=os.environ.copy())


 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__)]
   with patch_environment(omp_num_threads=1):
     execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,	env=os.environ.copy())



 @require_multi_gpu
 def   lowercase__  (       self):


   '''simple docstring'''




   print(f'Found {torch.cuda.device_count()} devices, using 2 devices only')
   lowercase__	: Optional[Any]     		=   ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
   with patch_environment(omp_num_threads=1       ,	cuda_visible_devices="""0,1"""):
     execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,	env=os.environ.copy())


if __name__ == "__main__":
     lowerCamelCase__     :		Any        = Accelerator()
     lowerCamelCase__     :		Union[str, Any]        = (accelerator.state.process_index + 2, 1_0)
     lowerCamelCase__     :		Tuple        = torch.randint(0, 1_0, shape).to(accelerator.device)

     lowerCamelCase__     :		List[Any]        = """"""

     lowerCamelCase__     :		str        = accelerator.pad_across_processes(tensor)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
          error_msg += "Padding was not done with the right value (0)."

     lowerCamelCase__     :		Any        = accelerator.pad_across_processes(tensor, pad_first=True)
     if tensora.shape[0] != accelerator.state.num_processes + 1:
          error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
     lowerCamelCase__     :		List[str]        = accelerator.state.num_processes - accelerator.state.process_index - 1
     if not torch.equal(tensora[index:], tensor):
          error_msg += "Tensors have different values."
     if not torch.all(tensora[:index] == 0):
          error_msg += "Padding was not done with the right value (0)."

     # Raise error at the end to make sure we don't stop at the first failure.
     if len(error_msg) > 0:
          raise ValueError(error_msg)







"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			["""ReformerTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[str]									=			["""ReformerTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:int									=			[
       """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ReformerAttention""",
       """ReformerForMaskedLM""",
       """ReformerForQuestionAnswering""",
       """ReformerForSequenceClassification""",
       """ReformerLayer""",
       """ReformerModel""",
       """ReformerModelWithLMHead""",
       """ReformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:int									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
